SemaGrow: Data intensive techniques to boost the real-time performance of global agricultural data infrastructures
SemaGrow (a) developed scalable and robust semantic storage and indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; (b) developed query decomposition, source selection, and distributed querying methods that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation; and (c) rigorously tested its components and overall architecture over real, complex, interconnected datasets comprising data and document collections, sensor data, and GIS data. SemaGrow is available on Github.
The main SemaGrow Stack is developed on github, as project semagrow/semagrow. Besides the SemaGrow Stack, the github.com/semagrow organization also publishes tools needed to configure the Stack, including:
1. semagrow/strHist for dynamically adapting data source descriptions from query feedback
2. semagrow/sevod-scraper for scraping data source descriptions from RDF dumps (forked from the now obsoleted metadatagen codebase)
semagrow/fork-eleon for manually authoring data source descriptions (forked from the main ELEON codebase)
3. See also the SemaGrow Knowledge Kit on Github, where Javadoc and other user and developer resources are published.
See also: https://smart-akis.com/SFCPPortal/#/app-h/technologies?techid=66
Smart-AKIS: European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research
in Smart Farming Technology
Ongoing | 2016-2018
in Smart Farming Technology
- Main funding source
- Horizon 2020 (EU Research and Innovation Programme)
- Geographical location
- Greece